Evaluation of biodegradable electric conductive tube-guides and mesenchymal stem cells

AIM: To study the therapeutic effect of three tubeguides with electrical conductivity associated to mesenchymal stem cells(MSCs) on neuro-muscular regeneration after neurotmesis.METHODS: Rats with 10-mm gap nerve injury were tested using polyvinyl alcohol(PVA), PVA-carbon nanotubes(CNTs) and MSCs, and PVA-polypyrrole(PPy). The regenerated nerves and tibialis anterior muscles were processed for stereological studies after 20 wk. The functional recovery was assessed serially for gait biomechanical analysis, by extensor postural thrust, sciatic functional index and static sciatic functionalindex(SSI), and by withdrawal reflex latency(WRL). In vitro studies included cytocompatibility, flow cytometry, reverse transcriptase polymerase chain reaction and karyotype analysis of the MSCs. Histopathology of lung, liver, kidneys, and regional lymph nodes ensured the biomaterials biocompatibility. RESULTS: SSI remained negative throughout and independently from treatment. Differences between treted groups in the severity of changes in WRL existed, showing a faster regeneration for PVA-CNTs-MSCs(P < 0.05). At toe-off, less acute ankle joint angles were seen for PVA-CNTs-MSCs group(P = 0.051) suggesting improved ankle muscles function during the push off phase of the gait cycle. In PVA-PPy and PVA-CNTs groups, there was a 25% and 42% increase of average fiber area and a 13% and 21% increase of the "minimal Feret's diameter" respectively. Stereological analysis disclosed a significantly(P < 0.05) increased myelin thickness(M), ratio myelin thickness/axon diameter(M/d) and ratio axon diameter/fiber diameter(d/D; g-ratio) in PVA-CNT-MSCs group(P < 0.05). CONCLUSION: Results revealed that treatment with MSCs and PVA-CNTs tube-guides induced better nerve fiber regeneration. Functional and kinematics analysis revealed positive synergistic effects brought by MSCs and PVA-CNTs. The PVA-CNTs and PVA-PPy are promising scaffolds with electric conductive properties, bio- and cytocompatible that might prevent the secondary neurogenic muscular atrophy by improving the reestablishment of the neuro-muscular junction.

Extraction of Phenolic Compounds from Olive Leaf Extracts and Their Effect on Proliferation of Human Carcinoma Cell Lines

The aim of this work was to evaluate the effect of different olive leaf extracts (OLE) from different leaf growing stages on human carcinoma cell lines. OLE were tested in human carcinoma cell lines in vitro and cells were plated in 96-microtiter culture plates for each OLE concentration. Fresh (F) and freeze-dried (FD) leaves exhibited phenolic compounds in the range of 2.09 ± 0.10 to 8.44 ± 0.64 and 7.72 ± 0.56 to 24.65 ± 1.9 mg gallic acid equivalents/g leaves, respectively. OLE from several Portuguese olive tree cultivars were found to inhibit the growth of human carcinoma cell lines in a range of 2.09 - 8.44 μg phenolic compound/well (209 - 844 μg/ml) and 0.07 - 2.40 μg phenolic compounds/well (7 - 240 μg/ml) for fresh and freeze-dried leaves, respectively. Young (Y) leaves have revealed the highest cell growth inhibition ranging from about 95% for Cobran?osa, followed by 90% for Cobran?osa, 90% for Arbequina and 75% for Arbequina for cell lines A549, HeLa, A431 and OE21, respectively. The lowest cell growth inhibition (35%) was observed for Galega (Y) leaf extract on cell line A549. However, FD samples exhibited a distinctive pattern since cell growth inhibition was highest at highest extract dilution tested, for A431 (Galega Y) followed by A549 (Cobran?osa Y) with cell inhibition of 75% and 70%, respectively. The data presented in this work strongly suggest that OLEs inhibit the growth of human carcinoma cell lines.

A Systematic Review of Computer Vision Techniques for Quality Control in End-of-Line Visual Inspection of Antenna Parts

The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.

Knowledge Level and Self-Reported Attitudes of Food Handlers: Case Study of a University Campus

This study was performed conducting surveys to assess the Knowledge Level (KL) and Self-Reported Attitudes (SRA) of Food Handlers (FH) in order to evaluate their food safety perception. Food handlers working in 5 cafes and 6 canteens in a university campus responded to a questionnaire about food hygiene. The knowledge level about food hygiene was obtained by answering five question groups (G): Agents involved in food borne diseases (G1), Food handling hygiene (G2), Cross contamination (G3), Heat treatment/cooling techniques (G4), Reduced temperatures (G5). The SRA level was obtained through seven questions with multiple choice options on behaviors of health and safety applied to the work routine, which were considered as percentage of hits. The hygiene’s knowledge average was 75%, ranging from 63.3% (G4) to 94.5% (G3). Significant associations between establishment type and G1 (p = 0.027), professional experience and G5 (p = 0.020), training and G5 (p = 0.037) were found. Food handlers knowledge did not have effect in FH attitude (p = 0.371). From 25 FH (46.0%) who had high KL, 17 had reported incorrect attitudes. The level of hits is, in general, more than 75%, except for matters relating to the use of different cutting tables (44.4%) and knives (51.9%). Significant differences of values and odds for handlers’ knowledge were observed between cafes’ and canteens’ FH. No difference (p > 0.05) was observed in FH SRA scores according to the type of establishment. The results reveal a reduced application of knowledge acquired by food handlers, evidenced by the low level of attitudes considered correct. This clearly justifies the implementation of additional measures, including on job training as part of an effective strategy to control establishment’s food safety.

Computational Fluid Dynamics Analysis of Greenhouses with Artificial Heat Tube

With the workmanship decrease in farms, the necessity to rationalize the use of other inputs and the development of technology has rapidly expanded the use of computer simulation in agricultural systems. One of the agricultural systems in which the modeling process of plant growth has been more engaged is the greenhouse production for horticultural crops. In Mediterranean climate, it is during the night that the energy losses are important and can be compensated with an artificial heat input. In this work an experiment was performed in a greenhouse in the north of Portugal. Temperature values in several points and air velocity in the aperture were measured during the night for three different cases: natural convective heating (case A);artificial heating tubes (AHT) (case B);AHT and natural ventilation (case C). A CFD simulation, carried out using FLOTRAN module of ANSYS, was also performed in two-dimensional configuration to obtain the indoor air temperature and velocity fields for the three cases. A very good agreement between experimental and numerical temperature values were verified, which allows to validate the adopted numerical procedure. In case A, the average temperature was 2.2℃. An average increase of 6.7℃ and 3.5℃ on the air temperature was obtained for the case B and case C, respectively. These results clearly emphasis the influence of each thermal load on greenhouse indoor air properties.

t-SNE:A study on reducing the dimensionality of hyperspectral data for the regression problem of estimating oenological parameters

In recent years there is a growing importance in using machine learning techniques to improve procedures in precision agriculture:in this work we perform a study on models capable of predicting oenological parameters from hyperspectral images of wine grape berries,a specially relevant topic to boost production tasks for winemakers.Specifically,we explore the capabilities of a novel technique mostly used for visualization,t-Distributed Stochastic Neighbor Embedding(t-SNE),for reducing the dimensionality of the highly complex hyperspectral data and compare its performance with Principal Component Analysis(PCA)method,which despite the introduction of many nonlinear dimensionality reduction techniques over the years,had achieved the best results for real-world data across several studies in literature.Additionally we explore the potential of Kernel t-SNE,an extension to the t-SNE method that allows for the usage of the technique in streaming data or online scenarios.Our results show that,in a direct comparison,t-SNE achieves better metrics than PCA for most of the data sets in this work and that the regressor(Support Vector Regression,SVR)performs better with the t-SNE reduced features as inputs,accomplishing better predictions with lower error rates.Comparing the results with current literature,our shallow learning model paired with t-SNE achieves either better or on par results than those reported,even competing with more advanced models that use deep learning techniques,which should propel the introduction of t-SNE in more studies that require dimensionality reduction.

Gold Supported on Metal Oxides for Carbon Monoxide Oxidation

Au 被装载了(1% wt ) 在不同广告上，氧化物支持(CuO， La2O3， Y2O3， NiO ) 由三个不同方法：两倍受精(暗淡) ，液体阶段减少的免职(LPRD ) ，和 ultrasonication (美国) 。样品被 N2 吸附描述在 ? 196 ??  ??????  ??  ? 洠浡業汬牡 ?  ???? 猠'T 業牣吗？

Proportionally Modular Diophantine Inequalities and Their Multiplicity

Let I be an interval of positive rational numbers. Then the set S （I） = T ∩ N, where T is the submonoid of （Q0＋, ＋） generated by T, is a numerical semigroup. These numerical semigroups are called proportionally modular and can be characterized as the set of integer solutions of a Diophantine inequality of the form ax rood b 〈 cx. In this paper we are interested in the study of the maximal intervals I subject to the condition that S （I） has a given multiplicity. We also characterize the numerical semigroups associated with these maximal intervals.